Huber-Shalem Revital, Hadar Ofer, Rotman Stanley R, Huber-Lerner Merav
Department of Communication Systems Engineering, Ben Gurion University of the Negev, Be'er Sheva, Israel.
Appl Opt. 2013 Mar 10;52(8):1646-54. doi: 10.1364/AO.52.001646.
Infrared (IR) imagery sequences are commonly used for detecting moving targets in the presence of evolving cloud clutter or background noise. This research concentrates on slow-moving point targets that are less than one pixel in size, such as aircraft at long ranges from a sensor. Because transmitting IR imagery sequences to a base unit or storing them consumes considerable time and resources, a compression method that maintains the point-target detection capabilities is highly desirable. In our previous work, we introduced two temporal compression methods that preserve the temporal profile properties of the point target in the form of discrete cosine transform (DCT) quantization and parabola fit. In the present work, we extend the compression task method of DCT quantization by applying spatial compression over the temporally compressed coefficients, which is followed by bit encoding. We evaluate the proposed compression method using a signal-to-noise ratio (SNR)-based measure for point target detection and find that it yields better results than the compression standard H.264. Furthermore, we introduce an automatic detection algorithm that extracts the target location from the SNR scores image, which is acquired during the evaluation process and has a probability of detection and a probability of false alarm close to those of the original sequences. We previously determined that it is necessary to establish a minimal noise level in the SNR-based measure to compensate for smoothing that is induced by the compression. Here, the noise level calculation process is modified in order to allow detection of targets traversing all background types.
红外（IR）图像序列通常用于在不断变化的云层杂波或背景噪声存在的情况下检测移动目标。本研究集中于尺寸小于一个像素的慢速移动点目标，例如距离传感器很远的飞机。由于将红外图像序列传输到基站单元或存储它们会消耗大量时间和资源，因此非常需要一种能够保持点目标检测能力的压缩方法。在我们之前的工作中，我们引入了两种时间压缩方法，它们以离散余弦变换（DCT）量化和抛物线拟合的形式保留了点目标的时间轮廓特性。在当前工作中，我们通过对时间压缩系数应用空间压缩，然后进行比特编码，扩展了DCT量化的压缩任务方法。我们使用基于信噪比（SNR）的点目标检测度量来评估所提出的压缩方法，发现它比压缩标准H.264产生更好的结果。此外，我们引入了一种自动检测算法，该算法从SNR分数图像中提取目标位置，该图像是在评估过程中获取的，其检测概率和误报概率与原始序列的接近。我们之前确定有必要在基于SNR的度量中建立一个最小噪声水平，以补偿压缩引起的平滑。在此，对噪声水平计算过程进行了修改，以便能够检测穿越所有背景类型的目标。
